The functional average treatment effect

Sparkes Shane (), Garcia Erika () and Zhang Lu ()
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Sparkes Shane: Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California 90033, United States
Garcia Erika: Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California 90033, United States
Zhang Lu: Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California 90033, United States

Journal of Causal Inference, 2024, vol. 12, issue 1, 30

Abstract: This article establishes the functional average as an important estimand for causal inference. The significance of the estimand lies in its robustness against traditional issues of confounding. We prove that this robustness holds even when the probability distribution of the outcome, conditional on treatment or some other vector of adjusting variables, differs almost arbitrarily from its counterfactual analogue. This article also examines possible estimators of the functional average, including the sample mid-range, and proposes a new type of bootstrap for robust statistical inference: the Hoeffding bootstrap. After this, the article explores a new class of variables, the U {\mathcal{U}} class, that simplifies the estimation of functional averages. This class of variables is also used to establish mean exchangeability in some cases and to provide the results of elementary statistical procedures, such as linear regression and the analysis of variance, with causal interpretations. Simulation evidence is provided. The methods of this article are also applied to a National Health and Nutrition Survey data set to investigate the causal effect of exercise on the blood pressure of adult smokers.

Keywords: causal inference; functional average; extreme order statistics; mean exchangeability; linear regression; Hoeffding bootstrap (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:30:n:1002

DOI: 10.1515/jci-2023-0076

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